The　Extended　Belief　Rule-Based　(EBRB)　system　has　been　widely　used　to　solve　the　real-world　problems　concerning　with　incompleteness,　uncertainty,　and　ambiguity.　However,　EBRB　is　essentially　a　data-driven　method,　in　which　each　rule　is　obtained　from　training　data.　Therefore,　the　generated　extended　belief　rules　may　be　severely　biased　when　dealing　with　data　with　imbalanced　classes.　In　this　case,　the　number　of　the　rules　generated　by　the　samples　of　majority　classes　(i.e.,　negative　samples)　may　be　much　larger　than　those　of　minority　classes　(i.e.,　positive　samples).　Thus,　the　class　imbalance　may　lead　to　significant　biases　in　system　decision-making.　In　order　to　resolve　this　problem,　this　paper　proposes　a　novel　EBRB　system　based　on　fuzzy　C-means　clustering　(FCM-EBRB).　First,　we　adopt　FCM　clustering　to　oversample　the　positive　samples　and　undersample　the　negative　ones,　so　as　to　achieve　the　balance　between　them.　Next,　this　paper　improves　the　construction　method　of　EBRB　and　optimizes　the　system　through　an　efficient　parameter　learning　strategy.　Finally,　this　paper　conducts　comprehensive　comparison　experiments　on　a　binary　classification　synthetic　dataset　and　11　commonly　used　KEEL　public　class　imbalance　datasets.　Experimental　results　show　that　the　proposed　method　can　effectively　reduce　the　scale　of　the　rule　base　and　achieve　high　inference　accuracy,　especially　for　imbalanced　data.　©　2021　Elsevier　B.V.